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- Ghorbanzad‘e, Mehdi, et al.
(author)
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Quantitative and qualitative prediction of corneal permeability for drug-like compounds
- 2011
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In: Talanta. - : Elsevier BV. - 0039-9140 .- 1873-3573. ; 85:5, s. 2686-2694
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Journal article (peer-reviewed)abstract
- A set of 69 drug-like compounds with corneal permeability was studied using quantitative and qualitative modeling techniques. Multiple linear regression (MLR) and multilayer perceptron neural network (MLP-NN) were used to develop quantitative relationships between the corneal permeability and seven molecular descriptors selected by stepwise MLR and sensitivity analysis methods. In order to evaluate the models, a leave many out cross-validation test was performed, which produced the statistic Q2 = 0.584 and SPRESS = 0.378 for MLR and Q2 = 0.774 and SPRESS = 0.087 for MLP-NN. The obtained results revealed the suitability of MLP-NN for the prediction of corneal permeability. The contribution of each descriptor to MLP-NN model was evaluated. It indicated the importance of the molecular volume and weight. The pattern recognition methods principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been employed in order to investigate the possible qualitative relationships between the molecular descriptors and the corneal permeability. The PCA and HCA results showed that, the data set contains two groups. Then, the same descriptors used in quantitative modeling were considered as inputs of counter propagation neural network (CPNN) to classify the compounds into low permeable (LP) and very low permeable (VLP) categories in supervised manner. The overall classification non error rate was 95.7% and 95.4% for the training and prediction test sets, respectively. The results revealed the ability of CPNN to correctly recognize the compounds belonging to the categories. The proposed models can be successfully used to predict the corneal permeability values and to classify the compounds into LP and VLP ones. Highlights ► Linear and nonlinear prediction of corneal permeability using molecular descriptors. ► MLP-NN model was found to be more successful than MLR equation. ► Molecular volume and molecular weight were identified as the most important descriptors. ► Categorizing drugs in two low permeable and very low permeable compounds groups. ► CPNN model can correctly recognize objects belonging to the groups.
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| 2. |
- Ghorbanzadeh, Mehdi, et al.
(author)
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Modeling the cellular uptake of magnetofluorescent nanoparticles in pancreatic cancer cells : a quantitative structure activity relationship study
- 2012
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In: Industrial & Engineering Chemistry Research. - : American Chemical Society (ACS). - 0888-5885 .- 1520-5045. ; 51:32, s. 10712-10718
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Journal article (peer-reviewed)abstract
- An artificial neural network was employed to predict the cellular uptake of 109 magnetofluorescent nanoparticles (NPs) in pancreatic cancer cells on the basis of quantitative structure activity relationship method. Six descriptors chosen by combining self organizing map and stepwise multiple linear regression (MLR) techniques were used to correlate the nanostructure of the studied particles with their bioactivity using MLR and multilayered perceptron neural network (MLP-NN) modeling techniques. For the MLR and MLP-NN models, the correlation coefficient was 0.769 and 0.934, and the root-mean-square error was 0.364 and 0.150, respectively. The results obtained after a leave-many-out cross-validation test revealed the credibility of MLP-NN for the prediction of cellular uptake of NPs. In addition, sensitivity analysis of MLP-NN model indicated that the number of hydrogen-bond donor sites in the organic coating of a NP is the predominant factor responsible for cellular uptake.
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